Perfusion in vessels or tissues may be detected by X-ray methods by injecting a contrast medium and detecting the time characteristic of the contrast medium concentration as a function of location. In order to acquire three-dimensional X-ray data sets, an examination object may be recorded from a plurality of recording angles and a volume data set may be reconstructed from the projection images. Numerous methods are known, for example a back projection of the projection images. If a condition of the examination object is anticipated to change of time, as is the case with perfusion scans owing to the changing contrast medium concentration, the reconstruction takes into account that the condition of the object changes over time between the individual projection images. Although different reconstruction options are known that take account of the change, a problem is that the time resolution of a corresponding scan is typically limited by the frequency of the recording of projection images in the individual recording geometries. The limitation inevitably results from the Nyquist-Shannon sampling theorem as long as prior knowledge about the system is not used.
The result is that for perfusion data acquisition, that uses a direct reconstruction, only X-ray devices that enable acquisition of projection images from a large number of recording angles in a very fast temporal sequence are suitable. If, however, acquisition of perfusion data is possible by way of a C-arm X-ray device, then sufficiently high recording rates are not attained.
To overcome corresponding limitations, prior knowledge about the examination object being monitored may be used, for example over the course of time of the contrast medium concentration within the context of perfusion scans. WO 2005/087 107 A1 discloses describing a time characteristic of the values of the individual voxels by way of a model function that is parameterized by location-dependent parameters. The parameters are determined by an iterative method.
One problem in the connection is that appropriate iterative methods are very computing-intensive and may include additional steps have to be taken to provide convergence or stability of the method.